We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| flodinl/neurips-1bCS | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| gen_rand_bern_matrix | Ran | flodinl/neurips-1bCS/1bcs_experiments.py pointer only (licence: NONE) · get_code("7f51f2e404be0cf0") |
| gen_rand_real_vector | Ran | flodinl/neurips-1bCS/1bcs_experiments.py pointer only (licence: NONE) · get_code("b68a1b6357d945f9") |
| sign_measure | Ran | flodinl/neurips-1bCS/1bcs_experiments.py pointer only (licence: NONE) · get_code("1f04fc7e3c3f441a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
One-bit compressed sensing (1bCS) is a method of signal acquisition under extreme measurement quantization that gives important insights on the limits of signal compression and analog-to-digital conversion. The setting is also equivalent to the problem of learning a sparse hyperplane-classifier. In this paper, we propose a novel approach for signal recovery in nonadaptive 1bCS that matches the sample complexity of the current best methods. We construct 1bCS matrices that are universal - i.e. work for all signals under a model - and at the same time recover very general random sparse signals with high probability. In our approach, we divide the set of samples (measurements) into two parts, and use the first part to recover the superset of the support of a sparse vector. The second set of measurements is then used to approximate the signal within the superset. While support recovery in 1bCS is well-studied, recovery of superset of the support requires fewer samples, and to our knowledge has not been previously considered for the purpose of approximate recovery of signals.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1910.13971")
get_code_for_paper("1910.13971")
have("1910.13971")
Connect an agent — have() is free.